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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.31

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/mag analysis pipeline. For information about how to interpret these results, please see the documentation.
        Report generated on 2026-02-07, 18:11 CET based on data in: /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/analysis/mag/work/88/696b30709ab4c3cff8742c875b28b3

        General Statistics

        Showing 39/39 rows and 17/35 columns.
        Sample Name% Dups (raw)Mean R1 LengthMean R2 Length% GC (raw)Avg. length (raw)Median length (raw)M Seqs (raw)% Fails (raw)% Dups (processed)% GC (processed)Avg. length (processed)Median length (processed)M Seqs (processed)% Fails (processed)% Duplication% > Q30Mb Q30 basesReads After FilteringGC content% PF% Adapter% Aligned (PhiX)% Aligned (Host)% Aligned (Assem.)N50 (Kbp)N50 (Kbp)Assembly Length (Mbp)Assembly Length (Mbp)CompletenessContaminationContigsBasesCDSOrganismClassification
        ERR3579731_run0
        42.0bp
        42.0bp
        3.6%
        91.9%
        899.0Mb
        20.7M
        49.9%
        76.1%
        2.0%
        0.0%
        3.1%
        ERR3579731_run0_raw_1
        4.4%
        50.0%
        43bp
        38bp
        13.6M
        9%
        ERR3579731_run0_raw_2
        4.4%
        50.0%
        43bp
        38bp
        13.6M
        9%
        ERR3579731_run0_trimmed_1
        4.4%
        50.0%
        47bp
        42bp
        10.1M
        9%
        ERR3579731_run0_trimmed_2
        4.4%
        50.0%
        47bp
        42bp
        10.1M
        9%
        ERR3579732_run0
        46.0bp
        46.0bp
        16.2%
        94.9%
        5040.5Mb
        101.1M
        56.5%
        75.3%
        0.3%
        0.0%
        6.3%
        ERR3579732_run0_raw_1
        16.7%
        56.0%
        46bp
        40bp
        67.1M
        9%
        ERR3579732_run0_raw_2
        15.5%
        56.0%
        46bp
        40bp
        67.1M
        9%
        ERR3579732_run0_trimmed_1
        17.6%
        57.0%
        52bp
        46bp
        47.4M
        9%
        ERR3579732_run0_trimmed_2
        16.2%
        57.0%
        52bp
        46bp
        47.4M
        9%
        ERR10114849_run0
        43.0bp
        43.0bp
        3.9%
        94.3%
        741.2Mb
        16.2M
        53.7%
        77.3%
        1.1%
        0.0%
        5.9%
        ERR10114849_run0_raw_1
        4.1%
        53.0%
        44bp
        38bp
        10.5M
        9%
        ERR10114849_run0_raw_2
        4.1%
        54.0%
        44bp
        38bp
        10.5M
        9%
        ERR10114849_run0_trimmed_1
        4.1%
        54.0%
        48bp
        42bp
        7.6M
        9%
        ERR10114849_run0_trimmed_2
        4.1%
        54.0%
        48bp
        42bp
        7.6M
        9%
        MEGAHIT-COMEBinRefined-ERR3579732.0_sub
        1.2Kbp
        1.1Mbp
        21.98%
        1.69%
        913
        1142775
        1396
        Genus species
        MEGAHIT-COMEBinRefined-ERR3579732.2964
        2.3Kbp
        3.3Mbp
        63.51%
        4.02%
        1586
        3327855
        3810
        Genus species
        MEGAHIT-COMEBinRefined-ERR3579732.2964.fa
        d__Bacteria;p__Actinomycetota;c__Actinomycetes;o__Mycobacteriales;f__Pseudonocardiaceae;g__Haloechinothrix_A;s__
        MEGAHIT-CONCOCTRefined-ERR3579732.13
        3.7Kbp
        3.3Mbp
        68.54%
        4.53%
        1002
        3311284
        3618
        Genus species
        MEGAHIT-CONCOCTRefined-ERR3579732.13.fa
        d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Burkholderiales;f__Burkholderiaceae;g__Polaromonas;s__
        MEGAHIT-CONCOCTRefined-ERR3579732.22
        4.7Kbp
        3.3Mbp
        69.29%
        1.66%
        787
        3342703
        3228
        Genus species
        MEGAHIT-CONCOCTRefined-ERR3579732.22.fa
        d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Burkholderiales;f__Burkholderiaceae;g__Acidovorax;s__
        MEGAHIT-ERR3579731
        17.1%
        0.9Kbp
        1.9Mbp
        MEGAHIT-ERR3579732
        31.0%
        1.3Kbp
        37.4Mbp
        MEGAHIT-ERR10114849
        13.4%
        0.8Kbp
        1.4Mbp
        MEGAHIT-SemiBin2Refined-ERR3579731.0
        1.2Kbp
        0.9Mbp
        56.12%
        1.87%
        792
        861955
        871
        Genus species
        MEGAHIT-SemiBin2Refined-ERR3579731.0.fa
        d__Bacteria;p__Bacillota;c__Clostridia;o__Peptostreptococcales;f__Anaerovoracaceae;g__RGIG7111;s__RGIG7111 sp036839975
        MEGAHIT-SemiBin2Refined-ERR3579732.6_sub
        1.3Kbp
        1.1Mbp
        27.09%
        1.77%
        990
        1133145
        1353
        Genus species
        MEGAHIT-SemiBin2Refined-ERR3579732.8_sub
        1.5Kbp
        2.3Mbp
        73.57%
        4.75%
        1745
        2308267
        2460
        Genus species
        MEGAHIT-SemiBin2Refined-ERR3579732.8_sub.fa
        d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Burkholderiales;f__Burkholderiaceae;g__Limnobacter;s__
        MEGAHIT-SemiBin2Refined-ERR3579732.19_sub
        0.9Kbp
        1.5Mbp
        52.12%
        8.57%
        1679
        1490271
        1765
        Genus species
        MEGAHIT-SemiBin2Refined-ERR3579732.19_sub.fa
        d__Bacteria;p__Methylomirabilota;c__Methylomirabilia;o__Methylomirabilales;f__JACPAU01;g__JACPAU01;s__
        MEGAHIT-SemiBin2Refined-ERR3579732.28
        3.0Kbp
        1.5Mbp
        94.23%
        2.80%
        646
        1535213
        1613
        Genus species
        MEGAHIT-SemiBin2Refined-ERR3579732.28.fa
        d__Archaea;p__Methanobacteriota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanocatella;s__Methanocatella sp963082495
        MEGAHIT-SemiBin2Refined-ERR3579732.31
        0.9Kbp
        0.9Mbp
        52.94%
        6.17%
        1049
        908554
        812
        Genus species
        MEGAHIT-SemiBin2Refined-ERR3579732.31.fa
        d__Bacteria;p__Bacillota;c__Bacilli;o__Staphylococcales;f__Gemellaceae;g__Gemella;s__
        MEGAHIT-SemiBin2Refined-ERR3579732.35
        23.5Kbp
        1.4Mbp
        91.84%
        0.35%
        112
        1445173
        1388
        Genus species
        MEGAHIT-SemiBin2Refined-ERR3579732.35.fa
        d__Bacteria;p__Bacillota;c__Clostridia;o__Peptostreptococcales;f__Anaerovoracaceae;g__RGIG7111;s__RGIG7111 sp036839975
        MEGAHIT-SemiBin2Refined-ERR10114849.1
        1.0Kbp
        0.6Mbp
        35.40%
        0.80%
        644
        594075
        582
        Genus species

        FastQC: raw reads

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        6 samples had less than 1% of reads made up of overrepresented sequences

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 1/1 rows and 3/3 columns.
        Overrepresented sequenceReportsOccurrences% of all reads
        GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
        2
        74156
        0.0406%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        fastp

        All-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).URL: https://github.com/OpenGene/fastpDOI: 10.1093/bioinformatics/bty560

        Fastp goes through fastq files in a folder and perform a series of quality control and filtering. Quality control and reporting are displayed both before and after filtering, allowing for a clear depiction of the consequences of the filtering process. Notably, the latter can be conducted on a variety of parameters including quality scores, length, as well as the presence of adapters, polyG, or polyX tailing.

        Filtered Reads

        Filtering statistics of sampled reads.

        Created with MultiQC

        Insert Sizes

        Insert size estimation of sampled reads.

        Created with MultiQC

        Sequence Quality

        Average sequencing quality over each base of all reads.

        Created with MultiQC

        GC Content

        Average GC content over each base of all reads.

        Created with MultiQC

        N content

        Average N content over each base of all reads.

        Created with MultiQC


        FastQC: after preprocessing

        After trimming and, if requested, contamination removal.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        6 samples had less than 1% of reads made up of overrepresented sequences

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 1/1 rows and 3/3 columns.
        Overrepresented sequenceReportsOccurrences% of all reads
        GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
        2
        50260
        0.0386%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        Bowtie2: PhiX removal

        Mapping statistics of reads mapped against PhiX and subsequently removed.URL: http://bowtie-bio.sourceforge.net/bowtie2; https://ccb.jhu.edu/software/hisat2DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4

        Paired-end alignments

        This plot shows the number of reads aligning to the reference in different ways.

        Please note that single mate alignment counts are halved to tally with pair counts properly.

        There are 6 possible types of alignment:

        • PE mapped uniquely: Pair has only one occurence in the reference genome.
        • PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
        • PE one mate mapped uniquely: One read of a pair has one occurence.
        • PE multimapped: Pair has multiple occurence.
        • PE one mate multimapped: One read of a pair has multiple occurence.
        • PE neither mate aligned: Pair has no occurence.
        Created with MultiQC

        Bowtie2: host removal

        Mapping statistics of reads mapped against host genome and subsequently removed.URL: http://bowtie-bio.sourceforge.net/bowtie2; https://ccb.jhu.edu/software/hisat2DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4

        Paired-end alignments

        This plot shows the number of reads aligning to the reference in different ways.

        Please note that single mate alignment counts are halved to tally with pair counts properly.

        There are 6 possible types of alignment:

        • PE mapped uniquely: Pair has only one occurence in the reference genome.
        • PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
        • PE one mate mapped uniquely: One read of a pair has one occurence.
        • PE multimapped: Pair has multiple occurence.
        • PE one mate multimapped: One read of a pair has multiple occurence.
        • PE neither mate aligned: Pair has no occurence.
        Created with MultiQC

        QUAST: assembly

        Assembly statistics of raw assemblies.URL: http://quast.bioinf.spbau.ruDOI: 10.1093/bioinformatics/btt086

        Assembly Statistics

        Showing 3/3 rows and 6/6 columns.
        Sample NameN50 (Kbp)N75 (Kbp)L50 (K)L75 (K)Largest contig (Kbp)Length (Mbp)
        MEGAHIT-ERR3579731
        0.9Kbp
        0.7Kbp
        0.7K
        1.3K
        9.0Kbp
        1.9Mbp
        MEGAHIT-ERR3579732
        1.3Kbp
        0.7Kbp
        6.4K
        16.5K
        49.6Kbp
        37.4Mbp
        MEGAHIT-ERR10114849
        0.8Kbp
        0.6Kbp
        0.5K
        1.0K
        23.7Kbp
        1.4Mbp

        Number of Contigs

        This plot shows the number of contigs found for each assembly, broken down by length.

        Created with MultiQC

        Bowtie2: assembly

        Mapping statistics of reads mapped against assemblies.URL: http://bowtie-bio.sourceforge.net/bowtie2; https://ccb.jhu.edu/software/hisat2DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4

        Paired-end alignments

        This plot shows the number of reads aligning to the reference in different ways.

        Please note that single mate alignment counts are halved to tally with pair counts properly.

        There are 6 possible types of alignment:

        • PE mapped uniquely: Pair has only one occurence in the reference genome.
        • PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
        • PE one mate mapped uniquely: One read of a pair has one occurence.
        • PE multimapped: Pair has multiple occurence.
        • PE one mate multimapped: One read of a pair has multiple occurence.
        • PE neither mate aligned: Pair has no occurence.
        Created with MultiQC

        QUAST: bins

        Assembly statistics of binned assemblies.URL: http://quast.bioinf.spbau.ruDOI: 10.1093/bioinformatics/btt086

        Assembly Statistics

        Showing 12/12 rows and 6/6 columns.
        Sample NameN50 (Kbp)N75 (Kbp)L50 (K)L75 (K)Largest contig (Kbp)Length (Mbp)
        MEGAHIT-COMEBinRefined-ERR3579732.0_sub
        1.2Kbp
        1.1Kbp
        0.4K
        0.6K
        3.2Kbp
        1.1Mbp
        MEGAHIT-COMEBinRefined-ERR3579732.2964
        2.3Kbp
        1.6Kbp
        0.5K
        0.9K
        10.6Kbp
        3.3Mbp
        MEGAHIT-CONCOCTRefined-ERR3579732.13
        3.7Kbp
        2.7Kbp
        0.3K
        0.6K
        49.6Kbp
        3.3Mbp
        MEGAHIT-CONCOCTRefined-ERR3579732.22
        4.7Kbp
        3.3Kbp
        0.2K
        0.4K
        22.9Kbp
        3.3Mbp
        MEGAHIT-SemiBin2Refined-ERR3579731.0
        1.2Kbp
        0.8Kbp
        0.3K
        0.5K
        4.5Kbp
        0.9Mbp
        MEGAHIT-SemiBin2Refined-ERR3579732.6_sub
        1.3Kbp
        0.9Kbp
        0.3K
        0.6K
        5.0Kbp
        1.1Mbp
        MEGAHIT-SemiBin2Refined-ERR3579732.8_sub
        1.5Kbp
        1.0Kbp
        0.5K
        1.0K
        7.1Kbp
        2.3Mbp
        MEGAHIT-SemiBin2Refined-ERR3579732.19_sub
        0.9Kbp
        0.7Kbp
        0.6K
        1.0K
        3.4Kbp
        1.5Mbp
        MEGAHIT-SemiBin2Refined-ERR3579732.28
        3.0Kbp
        1.9Kbp
        0.2K
        0.3K
        10.6Kbp
        1.5Mbp
        MEGAHIT-SemiBin2Refined-ERR3579732.31
        0.9Kbp
        0.7Kbp
        0.4K
        0.7K
        3.8Kbp
        0.9Mbp
        MEGAHIT-SemiBin2Refined-ERR3579732.35
        23.5Kbp
        13.2Kbp
        0.0K
        0.0K
        45.1Kbp
        1.4Mbp
        MEGAHIT-SemiBin2Refined-ERR10114849.1
        1.0Kbp
        0.7Kbp
        0.2K
        0.4K
        4.1Kbp
        0.6Mbp

        Number of Contigs

        This plot shows the number of contigs found for each assembly, broken down by length.

        Created with MultiQC

        CheckM

        Estimates genome completeness and contamination based on the presence or absence of marker genes.URL: https://github.com/Ecogenomics/CheckMDOI: 10.1101/gr.186072.114

        Bin quality

        The quality of microbial genomes recovered from isolates, single cells, and metagenomes.

        An automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes.

        Showing 12/12 rows and 6/6 columns.
        Bin IdMarker lineageGenomesMarkersMarker setsCompletenessContamination
        MEGAHIT-COMEBinRefined-ERR3579732.0_sub
        p__Proteobacteria (UID3880)
        1495
        261
        164
        21.98%
        1.69%
        MEGAHIT-COMEBinRefined-ERR3579732.2964
        o__Actinomycetales (UID2012)
        35
        495
        282
        63.51%
        4.02%
        MEGAHIT-CONCOCTRefined-ERR3579732.13
        o__Burkholderiales (UID4000)
        193
        427
        214
        68.54%
        4.53%
        MEGAHIT-CONCOCTRefined-ERR3579732.22
        o__Burkholderiales (UID4105)
        54
        553
        264
        69.29%
        1.66%
        MEGAHIT-SemiBin2Refined-ERR3579731.0
        o__Clostridiales (UID1120)
        304
        247
        141
        56.12%
        1.87%
        MEGAHIT-SemiBin2Refined-ERR3579732.6_sub
        o__Burkholderiales (UID4000)
        193
        427
        214
        27.09%
        1.77%
        MEGAHIT-SemiBin2Refined-ERR3579732.8_sub
        c__Betaproteobacteria (UID3888)
        323
        387
        234
        73.57%
        4.75%
        MEGAHIT-SemiBin2Refined-ERR3579732.19_sub
        k__Bacteria (UID3187)
        2258
        188
        117
        52.12%
        8.57%
        MEGAHIT-SemiBin2Refined-ERR3579732.28
        p__Euryarchaeota (UID3)
        148
        188
        125
        94.23%
        2.80%
        MEGAHIT-SemiBin2Refined-ERR3579732.31
        c__Bacilli (UID285)
        586
        318
        174
        52.94%
        6.17%
        MEGAHIT-SemiBin2Refined-ERR3579732.35
        o__Clostridiales (UID1120)
        304
        247
        141
        91.84%
        0.35%
        MEGAHIT-SemiBin2Refined-ERR10114849.1
        o__Clostridiales (UID1120)
        304
        247
        141
        35.40%
        0.80%

        Prokka

        Rapid annotation of prokaryotic genomes.URL: http://www.vicbioinformatics.com/software.prokka.shtmlDOI: 10.1093/bioinformatics/btu153

        This barplot shows the distribution of different types of features found in each contig.

        Prokka can detect different features:

        • CDS
        • rRNA
        • tmRNA
        • tRNA
        • miscRNA
        • signal peptides
        • CRISPR arrays

        This barplot shows you the distribution of these different types of features found in each contig.

        Created with MultiQC

        GTDB-Tk

        Assigns objective taxonomic classifications to bacterial and archaeal genomes.URL: https://ecogenomics.github.io/GTDBTk/index.htmlDOI: 10.1093/bioinformatics/btac672

        MAG taxonomy

        The taxonomy of a MAG as found by GTDB.

        GTDB-Tk is a software toolkit for assigning objective taxonomic classifications to bacterial and archaeal genomes based on the Genome Database Taxonomy GTDB. It is designed to work with recent advances that allow hundreds or thousands of metagenome-assembled genomes (MAGs) to be obtained directly from environmental samples. It can also be applied to isolate and single-cell genomes.

        Showing 9/9 rows and 6/8 columns.
        User genomeClassificationFull classificationClassification methodANI to closest genomeAF to closest genomeREDWarningsNotes
        MEGAHIT-COMEBinRefined-ERR3579732.2964.fa
        s__
        d__Bacteria; p__Actinomycetota; c__Actinomycetes; o__Mycobacteriales; f__Pseudonocardiaceae; g__Haloechinothrix_A; s__
        taxonomic classification defined by topology and ANI
        1.0
        classification based on placement in class-level tree
        MEGAHIT-CONCOCTRefined-ERR3579732.13.fa
        s__
        d__Bacteria; p__Pseudomonadota; c__Gammaproteobacteria; o__Burkholderiales; f__Burkholderiaceae; g__Polaromonas; s__
        taxonomic classification defined by topology and ANI
        1.0
        classification based on placement in class-level tree
        MEGAHIT-CONCOCTRefined-ERR3579732.22.fa
        s__
        d__Bacteria; p__Pseudomonadota; c__Gammaproteobacteria; o__Burkholderiales; f__Burkholderiaceae; g__Acidovorax; s__
        taxonomic classification defined by topology and ANI
        1.0
        classification based on placement in class-level tree
        MEGAHIT-SemiBin2Refined-ERR3579731.0.fa
        s__RGIG7111 sp036839975
        d__Bacteria; p__Bacillota; c__Clostridia; o__Peptostreptococcales; f__Anaerovoracaceae; g__RGIG7111; s__RGIG7111 sp036839975
        taxonomic classification defined by topology and ANI
        99.7
        0.9
        topological placement and ANI have congruent species assignments
        MEGAHIT-SemiBin2Refined-ERR3579732.8_sub.fa
        s__
        d__Bacteria; p__Pseudomonadota; c__Gammaproteobacteria; o__Burkholderiales; f__Burkholderiaceae; g__Limnobacter; s__
        taxonomic classification defined by topology and ANI
        1.0
        classification based on placement in class-level tree
        MEGAHIT-SemiBin2Refined-ERR3579732.19_sub.fa
        s__
        d__Bacteria; p__Methylomirabilota; c__Methylomirabilia; o__Methylomirabilales; f__JACPAU01; g__JACPAU01; s__
        taxonomic classification fully defined by topology
        1.0
        Genome has more than 10.0% of markers with multiple hits
        classification based on placement in class-level tree
        MEGAHIT-SemiBin2Refined-ERR3579732.28.fa
        s__Methanocatella sp963082495
        d__Archaea; p__Methanobacteriota; c__Methanobacteria; o__Methanobacteriales; f__Methanobacteriaceae; g__Methanocatella; s__Methanocatella sp963082495
        ani_screen
        99.2
        0.9
        classification based on ANI only
        MEGAHIT-SemiBin2Refined-ERR3579732.31.fa
        s__
        d__Bacteria; p__Bacillota; c__Bacilli; o__Staphylococcales; f__Gemellaceae; g__Gemella; s__
        taxonomic classification defined by topology and ANI
        1.0
        classification based on placement in class-level tree
        MEGAHIT-SemiBin2Refined-ERR3579732.35.fa
        s__RGIG7111 sp036839975
        d__Bacteria; p__Bacillota; c__Clostridia; o__Peptostreptococcales; f__Anaerovoracaceae; g__RGIG7111; s__RGIG7111 sp036839975
        ani_screen
        99.8
        1.0
        classification based on ANI only

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        ADJUST_MAXBIN2_EXTcoreutils9.5
        ALEale20180904
        BCFTOOLS_CONSENSUSbcftools1.22
        BCFTOOLS_INDEXbcftools1.22
        BCFTOOLS_VIEWbcftools1.22
        BIN_SUMMARYpandas1.4.3
        python3.10.6
        BOWTIE2_ASSEMBLY_ALIGNbowtie22.4.2
        pigz2.3.4
        samtools1.11
        BOWTIE2_ASSEMBLY_BUILDbowtie22.4.2
        BOWTIE2_HOST_REMOVAL_ALIGNbowtie22.4.2
        BOWTIE2_HOST_REMOVAL_BUILDbowtie22.4.2
        BOWTIE2_PHIX_REMOVAL_ALIGNbowtie22.4.2
        BOWTIE2_PHIX_REMOVAL_BUILDbowtie22.4.2
        CHECKM_LINEAGEWFcheckm1.2.3
        CHECKM_QAcheckm1.2.3
        COMEBIN_RUNCOMEBINcomebin1.0.4
        CONCAT_CHECKM_TSVqsv5.1.0
        CONCOCT_CONCOCTconcoct1.1.0
        CONCOCT_CONCOCTCOVERAGETABLEconcoct1.1.0
        CONCOCT_CUTUPFASTAconcoct1.1.0
        CONCOCT_EXTRACTFASTABINSconcoct1.1.0
        CONCOCT_MERGECUTUPCLUSTERINGconcoct1.1.0
        CONVERT_DEPTHSbioawk20110810
        DASTOOL_DASTOOLdastool1.1.7
        DASTOOL_FASTATOCONTIG2BINdastool1.1.7
        FAIDXsamtools1.22.1
        FASTQC_RAWfastqc0.12.1
        FASTQC_TRIMMEDfastqc0.12.1
        FREEBAYESfreebayes1.3.10
        GTDBTK_CLASSIFYWFgtdb_dbr226
        gtdbtk2.5.2
        GTDBTK_SUMMARYpandas1.4.3
        python3.10.6
        GUNZIP_BINSgunzip1.13
        GUNZIP_SHORTREAD_ASSEMBLIESgunzip1.13
        MAG_DEPTHSpandas1.1.5
        python3.6.7
        MAG_DEPTHS_SUMMARYpandas1.4.3
        python3.10.6
        MAXBIN2maxbin22.2.7
        MEGAHITmegahit1.2.9
        METABAT2_JGISUMMARIZEBAMCONTIGDEPTHS_SHORTREADmetabat22.15
        METABAT2_METABAT2metabat22.17
        METABINNER_BINSpython3.7.6
        METABINNER_KMERMetaBinner1.4.4-0
        python3.7.6
        METABINNER_METABINNERMetaBinner1.4.4-0
        python3.7.6
        METABINNER_TOOSHORTMetaBinner1.4.4-0
        python3.7.6
        PYDAMAGE_ANALYZEpydamage1.0
        PYDAMAGE_FILTERpydamage1.0
        Prokkaprokka1.14.6
        QUASTmetaquast5.0.2
        python3.7.6
        QUAST_BINSmetaquast5.0.2
        python3.7.6
        QUAST_BINS_SUMMARYcp9.5
        RENAME_POSTDASTOOLcoreutils9.5
        RENAME_PREDASTOOLcoreutils9.5
        SEMIBIN_SINGLEEASYBINSemiBin2.2.0
        SEQKIT_STATSseqkit2.9.0
        SPLIT_FASTAbiopython1.7.4
        pandas1.1.5
        python3.6.7
        SUMMARISE_PYDAMAGEBINSpandas1.4.3
        python3.10.6
        WorkflowNextflow25.04.2
        nf-core/magv5.4.0-gb52fa53
        fastpfastp1.0.1

        nf-core/mag Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/nf-core/mag

        Methods

        Data was processed using nf-core/mag v5.4.0 ((doi: 10.1093/nargab/lqac007); Krakau et al., 2022) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v25.04.2 (Di Tommaso et al., 2017) with the following command:

        nextflow run nf-core/mag -r 5.4.0 -profile conda,eva_grace --input /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/analysis/mag/AncientMetagenomeDir_nf_core_mag_input_paired_table.csv --outdir /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/analysis/mag/results_params --igenomes_base 's3://ngi-igenomes/igenomes/' --checkm_db /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/cache/database/checkm_data_2015_01_16 --gunc_db /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/cache/database/gunc_db/gunc_db_progenomes2.1.dmnd --gtdb_db /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/cache/database/release226 -c /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/analysis/mag/custom.conf -params-file /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/analysis/mag/nf-params.json -with-tower

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Krakau, S., Straub, D., Gourlé, H., Gabernet, G., & Nahnsen, S. (2022). nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning. NAR Genomics and Bioinformatics, 4(1). https://doi.org/10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        nf-core/mag Workflow Summary

        Input/output options

        input
        /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/analysis/mag/AncientMetagenomeDir_nf_core_mag_input_paired_table.csv
        outdir
        /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/analysis/mag/results_params

        Institutional config options

        config_profile_contact
        James Fellows Yates (@jfy133)
        config_profile_description
        MPI-EVA GRACE cluster profile provided by nf-core/configs.
        config_profile_url
        https://eva.mpg.de

        Generic options

        trace_report_suffix
        2026-02-06_14-21-09

        Quality control for short reads options

        host_genome
        GRCh37
        reads_minlength
        30

        Taxonomic profiling options

        gtdb_db
        /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/cache/database/release226
        gtdbtk_max_contamination
        10.0
        gtdbtk_min_completeness
        50.0

        Assembly options

        skip_spades
        true
        skip_spadeshybrid
        true

        Gene prediction and annotation options

        skip_metaeuk
        true
        skip_prodigal
        true

        Binning options

        binning_map_mode
        own
        exclude_unbins_from_postbinning
        true
        min_contig_size
        500
        save_assembly_mapped_reads
        true

        Bin quality check options

        checkm_db
        /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/cache/database/checkm_data_2015_01_16
        gunc_db
        /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/cache/database/gunc_db/gunc_db_progenomes2.1.dmnd
        postbinning_input
        refined_bins_only
        refine_bins_dastool
        true
        refine_bins_dastool_threshold
        0.3
        run_checkm
        true
        run_gunc
        true

        Ancient DNA assembly

        ancient_dna
        true

        Core Nextflow options

        configFiles
        /home/james_fellows_yates/.nextflow/config, /home/james_fellows_yates/.nextflow/assets/nf-core/mag/nextflow.config, /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/analysis/mag/custom.conf
        launchDir
        /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/analysis/mag
        profile
        conda,eva_grace
        projectDir
        /home/james_fellows_yates/.nextflow/assets/nf-core/mag
        revision
        5.4.0
        runName
        jovial_kalman
        userName
        james_fellows_yates
        workDir
        /mnt/archgen/users/james_fellows_yates/mag/nfcore-mag-adna-protocol/analysis/mag/work